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This book is still under construction. We appreciate your patience as we get it completed. Feedback is welcome!

Introduction

Regression

1. Linear Regression
2. Assessing Performance
3. Ridge Regularization
4. Feature Selection and LASSO Regularization

Classification

5. Classification Overview
6. Logistic Regression
7. Bias and Fairness
8. Naïve Bayes
9. Decision Trees
10. Ensemble Methods

Deep Learning

11. Neural Networks (coming soon)
12. Convolutional Neural Networks (coming soon)

Document Retrieval / Local Methods

13. Introduction, Precision/Recall, k-Nearest Neighbors (coming soon)
14. Kernel Methods (coming soon)
15. Locality Sensitive Hashing (coming soon)
16. Clustering & k-means (coming soon)
17. Hierarchical Clustering (coming soon)

Recommender Systems

18. Dimensionality Reduction (coming soon)
19. Recommender Systems

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By Hunter Schafer

© Copyright 2023.

Have feedback or spotted a bug? Please make a GitHub issue or contact Hunter Schafer!